Ultrasound Enhanced Delivery of Molecular Imaging and Therapeutic Agents in Alzheimer's Disease Mouse Models

Alzheimer's disease is a neurodegenerative disorder typified by the accumulation of a small protein, beta-amyloid, which aggregates and is the primary component of amyloid plaques. Many new therapeutic and diagnostic agents for reducing amyloid plaques have limited efficacy in vivo because of poor transport across the blood-brain barrier. Here we demonstrate that low-intensity focused ultrasound with a microbubble contrast agent may be used to transiently disrupt the blood-brain barrier, allowing non-invasive, localized delivery of imaging fluorophores and immunotherapeutics directly to amyloid plaques. We administered intravenous Trypan blue, an amyloid staining red fluorophore, and anti-amyloid antibodies, concurrently with focused ultrasound therapy in plaque-bearing, transgenic mouse models of Alzheimer's disease with amyloid pathology. MRI guidance permitted selective treatment and monitoring of plaque-heavy anatomical regions, such as the hippocampus. Treated brain regions exhibited 16.5±5.4-fold increase in Trypan blue fluorescence and 2.7±1.2-fold increase in anti-amyloid antibodies that localized to amyloid plaques. Ultrasound-enhanced delivery was consistently reproduced in two different transgenic strains (APPswe:PSEN1dE9, PDAPP), across a large age range (9–26 months), with and without MR guidance, and with little or no tissue damage. Ultrasound-mediated, transient blood-brain barrier disruption allows the delivery of both therapeutic and molecular imaging agents in Alzheimer's mouse models, which should aid pre-clinical drug screening and imaging probe development. Furthermore, this technique may be used to deliver a wide variety of small and large molecules to the brain for imaging and therapy in other neurodegenerative diseases.

Introduction
Neurodegenerative diseases are difficult to treat, in part because of the blood-brain barrier (BBB), a composite of highly specialized endothelial and perivascular structures that limit transport of molecules into the brain. Medicinal chemists must extensively modify drugs in order to bypass the BBB, at significant cost and delayed time to clinic. Many large biological molecules, such as antibodies and siRNA, cannot be easily modified to cross the BBB passively. Although several promising strategies have been developed for harnessing endogenous active transport systems [1,2], these techniques have limited carrying capacity and must be tailor-made for the application. A generic, non-invasive technique to deliver drugs to the brain would allow preclinical efficacy screening and facilitate basic research with biological agents.
We and others have shown that low-intensity, focused ultrasound (FUS) administered with microbubbles (MB) (here termed FUS-MB therapy), can transiently disrupt the blood-brain barrier [3,4]. At frequencies suitable for non-invasive trans-skull focusing [5], FUS-MB causes BBB-disruption only in the transducer focal volume, which can be selected in real-time by magnetic resonance (MR) imaging for precise anatomical delivery. FUS-MB has been used to deliver antibodies, including Herceptin [6,7] and small-molecule chemotherapeutics [8], in wild-type animals. To date, FUS-MB has not been applied to neurodegenerative disorders, a broad class of diseases that is largely refractory to current diagnostic and therapeutic approaches.
Alzheimer's disease (AD) is a progressive and fatal neurodegenerative disorder that afflicts millions worldwide and has no cure. AD pathogenesis is putatively tied to the accumulation of misfolded proteins, beta-amyloid (Ab) and hyperphosphorylated tau, which aggregate and form amyloid plaques and tau tangles [9]. Emerging diagnostic and therapeutic strategies focus on detecting and reducing Ab aggregates [10,11]. One promising strategy for amyloid plaque clearance is immunization against Ab, either by active vaccination with Ab or by administration of anti-Ab antibodies, termed passive immunization [12]. Screening anti-Ab antibodies in transgenic AD mouse models generally requires high, repeated dosing over months, which is enormously expensive due to the cost of purified antibody and transgenic mouse strains. We believe that FUS-MB might alleviate some of this cost by allowing localized delivery of anti-Ab antibodies at high concentration.
In undertaking this study, we were concerned that differences between aged transgenics and wild-type mice might affect FUS-MB. For example, aged transgenic mice (often .1 year old) generally have thicker, more brittle skulls and altered vascular physiology. Additionally, we wanted to use a simple benchtop sonication system to allow high throughput FUS-MB for drug studies. Here we demonstrate that FUS-MB enhances both smallmolecule and antibody delivery in transgenic AD mice and can be achieved without MRI guidance in a simple benchtop setup.

MRI-Guided Focused Ultrasound-Microbubble Treatment
We initially attempted small-molecule delivery using a proven MRI-guided sonication protocol [7]. For a targeted imaging probe, we chose Trypan blue, a bis-azo red fluorescent dye that shares structural similarity with Congo red ( Figure S1) and also binds amyloid [13]. Normally, Trypan blue does not cross the BBB because of its size (MW, 916 Da) and hydrophilicity (4 sulfonic acid groups). When the BBB is disrupted, Trypan blue extravasates into tissue, resulting in a grossly visible blue stain [14], and thus has been used classically to demonstrate BBB breakdown.
Aged (11-12 month-old), double transgenic mice (n = 2) expressing APPswe and PSEN1dE9 mutations were treated with MRI-guided FUS-MB (Figure 1), and then received Trypan blue. BBB-disruption was confirmed in vivo with gadolinium-based, contrast-enhanced MRI ( Figure 2). The millimeter-scale anatomical structures selected pre-treatment were correctly targeted and exhibited BBB-disruption on T1-weighted fast spin-echo (FSE) MR images. Gadolinium-based contrast-enhancement was observed in a ,2 mm65 mm ellipsoid confined to the right hemisphere and traversing the brain from cortex to thalamus, apparent in the coronal plane ( Figure 2). We observed some contrast in the right ventricle, immediately adjacent to the intended target position (blue ''+'', Figure 2A-B).
Post-mortem brains exhibited a distinct blue spot, confined to a sub-region approximating the dimensions of the transducer focus, from Trypan blue extravasation across the BBB. No blue staining was grossly observed in the contralateral hemisphere. On 50-mm coronal sections, Trypan blue fluorescence was significantly higher in the treated volume (p = 0.02, paired t-test, comparing treated and untreated regions of interest (ROIs), n = 5 animals including 3 from benchtop experiments below) and appeared to concentrate at small plaque-like inclusions in the cortex, hippocampus, and to a lesser extent, in the thalamus ( Figure 2E).
We confirmed that the Trypan blue-stained aggregates were amyloid plaques with immunohistochemistry (IHC) using a FITCconjugated anti-Ab antibody (3d6) ( Figure 2F). In the sonicated volume, Trypan blue (red) co-localized with FITC-3d6 signal (green) and stained dense core plaques. When normalized for background autofluorescence, average Trypan blue fluorescence was up to 21-fold higher (16.565.4, including benchtop experiments from below) in the treated vs. untreated cortex. These results indicate that FUS-MB significantly increased Trypan blue delivery within the sonication volume and that Trypan blue  successfully diffused from the blood vessels to an extravascular target, amyloid plaques.

Aged PDAPP Mice
To test the robustness of FUS-MB across transgenic strains, we repeated the treatment described above in 26 month-old PDAPP mice (n = 2), which express a mutant form of APP that results in severe amyloidosis and accompanying pathology, including neuritic dystrophies, astrocytosis, and microgliosis [15]. We observed BBB disruption on contrast-enhanced, T1-weighted FSE imaging following FUS-MB and from Trypan blue staining in the sonicated hemisphere of post-mortem brains ( Figure S2). Thus, FUS-MB was effective in two different transgenic AD models, even in extensively aged mice.

Benchtop Focused Ultrasound-Microbubble Therapy
We next tested the capabilities of a MRI-free, benchtop system by administering Trypan blue or Evans blue, a Trypan blue isoform ( Figure S1), to transgenic mice (n = 6, 1 sham-treated) 10 minutes following FUS-MB therapy. The entire procedure took approximately 5 minutes per animal, compared to 30-45 minutes with the MR-guided procedure. All treated mice had distinct blue staining in a focal region of the right hemisphere on post-mortem examination. We did not observe blue staining in the sham-treated animal. Sonication locations, determined from blue staining, were within 2.2 mm of the intended location and accuracy depended upon the system operator ( Figure S3). These results confirmed that FUS-MB is possible in transgenic animals using our simple benchtop sonication system, albeit with poorer localization accuracy.

Focused Ultrasound-Microbubble Enhanced Antibody Delivery
Although small molecule delivery has some application in AD therapy and diagnostics, we were primarily interested in the delivery of much larger biological molecules, such as antibodies, for AD therapy. Two previous results suggested that antibody delivery should be possible in aged transgenic mice. First, experiments by Kinoshita et al demonstrated antibody delivery to young, wild-type mice using FUS-MB [7]. Furthermore, in our first FUS-MB experiments in transgenic AD mice, we saw evidence of endogenous mouse IgG extravasation. When we stained the FUS-MB treated brains with Alexa 488-conjugated goat anti-mouse IgG antibodies (Invitrogen, Eugene, OR, USA), we observed diffuse fluorescence throughout the sonicated volume, 10 to 20-fold greater than in the untreated hemisphere, indicative of endogenous mouse IgG that had extravasated across the BBB ( Figure 3A).
We next attempted the delivery of Ab-targeted antibodies by administering rabbit anti-Ab antibodies (Anti-b-Amyloid (1-40) developed in rabbit, whole antiserum, A 8326, Sigma Aldrich, St. Louis, Missouri) to transgenic AD mice immediately before benchtop FUS-MB. Following perfusion and tissue sectioning, we stained for the rabbit antibody using Alexa Fluor 350 conjugated goat anti-rabbit IgG antibodies (Invitrogen, Eugene, OR, USA). We observed stronger Alexa Fluor 350 fluorescence (blue) in the treated hemisphere (p = 0.06, paired t-test, n = 3, comparing treated and untreated ROIs normalized to control tissue) which coincided with Trypan blue-stained amyloid plaques (see Figure 4).
Alexa Fluor 350 fluorescence did not perfectly match Trypan blue fluorescence and was somewhat heterogeneous throughout the sonicated volume, with stronger fluorescence in the hippocampus and around a few large blood vessels ( Figure 4A). In these sub-regions, there was strong background staining, which we interpreted as extravasated, non-specific rabbit IgG, which is the primary constituent of the whole antiserum preparation. On the microscopic level, Alexa Fluor 350 fluorescence colocalized with Trypan blue-positive plaques and vascular amyloid ( Figure 4D-F).

Histological Evaluation
We used hemotoxylin and eosin (H&E) stained tissue sections to assess FUS-MB induced tissue damage in post-mortem brain sections. 3 of 9 brains had 2-10 scattered petechiae on individual 50-mm H&E sections ( Figure 5); 1 of these had petechiae on the inferior aspect of the brain and in the thalamus, along the axis of the transducer. When we compared histological effects with the peak negative pressure applied, we found that the 2 animals treated at high pressure (estimated 0.8 MPa) had petechiae, whereas only 1 of 7 treated at a lower pressure (estimated 0.67 MPa) had petechiae. These results were consistent with other histological studies of FUS-MB damage in rabbit and mice at similar ultrasound parameters [6,7,16].

Discussion
These experiments demonstrated that ultrasound can enhance the delivery of small fluorescent agents and large biological immunotherapeutics in transgenic mouse models of Alzheimer's disease. Depending on the strain used, transgenic AD mice are aged 9-15 months before they develop amyloid plaques and other neurological features of AD. The young, wild-type mouse skull attenuates the acoustic pressure by 1367% [6], whereas the transgenic AD mouse skull, considerably thicker and more brittle, could potentially attenuate the ultrasound field even further and might shift the focal coordinate position. Despite these differences between transgenic and wild-type animals, we observed robust Trypan blue delivery in two different transgenic strains for a wide range of ages (9-26 months). This suggests that, at least at the low frequency used here (f = 0.69 MHz), impedance effects from the skull could be overcome.
Additionally, some AD transgenic strains have altered vascular physiology, including increased BBB permeability and risk of hemorrhage and other vascular accidents [17,18,19,20]. Although there was some evidence for low Trypan blue leakage in the untreated hemisphere, FUS-MB increased Trypan blue fluorescence 16.5-fold in the treated region ( Figure 3B). Furthermore, we did not see increased histological tissue damage in transgenic mice compared to previous studies with wild-type mice and rabbits. Only 1 of 7 mice in this study treated at the lowest pressure amplitude (0.67 MPa) exhibited petechiae. Thus, FUS-MB delivery of small molecules can be achieved with minimal histological damage and markedly increases brain dosage compared to background BBB ''leak'' in transgenic AD mouse models.
Similar dosage enhancement was seen for antibodies ( Figure 3B). We measured a 10-20 fold fluorescence increase for endogenous IgG in the treated compared to untreated regions and a ,3-fold fluorescence increase for rabbit anti-Ab IgG. These estimates were made by comparing average fluorescence from treated and untreated regions of interest on stained brain sections, with normalization to background, untreated tissue, and are therefore only semi-quantitative estimates of relative dose. It is unclear why endogenous IgG leakage was greater than IV-administered antibodies. Because IV-administered antibodies were at a significantly lower concentration than endogenous IgG, it is likely that this discrepancy represents the limited sensitivity of IHC staining techniques, as well as tissue fixation, fluorescence quenching, imaging parameters, and image analysis. However, we cannot rule out inherently different pharmacokinetics of rabbit vs. mouse IgG in a mouse model.
Rabbit anti-Ab delivery had a heterogeneous distribution within the transducer focus, with enhanced Alexa Fluor 350 staining around large blood vessels and in the hippocampus. This  heterogeneity was similar to the delivery distribution observed by Kinoshita et al [7], and might be explained by the limited diffusion of antibodies within the neuropil or the mechanisms of FUS-MB mediated BBB disruption. Electron microscopy studies of FUS-MB enhanced delivery suggest that, following FUS-MB, large molecules cross the BBB via para-endothelial passages (through breached tight junctions) or by trans-endothelial, vesicular transport [21]. These two separate mechanisms were further corroborated by real-time multiphoton imaging that demonstrated kinetically distinct delivery processes [22]. Vesicular transport is preferentially upregulated in arterioles [23], and thus might be heterogeneously activated across brain anatomy depending on the relative density of arterioles. Further work is needed to determine if adequate homogeneity can be achieved for therapeutic efficacy of anti-Ab antibodies.
Others have suggested that the targeting capabilities of FUS-MB could be applied for drug delivery to small anatomical targets, such as the hippocampus [24]. We successfully targeted amyloid plaques within hippocampus using MRI-guidance, but had some ''off-target'' delivery in the cortex and thalamus due to the dimensions of our transducer focal volume (,2 mm614 mm, ellipsoid). This limitation could be resolved by using a larger diameter transducer with a tighter focal volume.
Targeting with our benchtop system was less accurate. From the cohort described here, targeting was within 2.2 mm of the intended location and varied with the system user (see Figure  S3). Variability was likely due largely to animal motion and inaccurate animal positioning. Others have used a more traditional stereo-tactic approach, with the head fixed in ear-bars, and sonication from an applicator [24,25], with better accuracy.
Because benchtop FUS-MB can be performed with a simple setup in a short procedure (5 min), it offers the possibility for highthroughput anti-Ab antibody screening. It is unclear from these experiments if a single FUS-MB enhanced anti-Ab antibody dose is adequate for amyloid clearance. Future work will quantify antibody dose and amyloid clearance in treated animals.
Besides antibodies, FUS-MB may be used to deliver a variety of imaging and therapeutic molecules to the transgenic mouse brain, including newly developed amyloid-targeted MR probes [26,27] and siRNA [28,29]. We believe that FUS-MB may be used for imaging and drug delivery in transgenic models of neurodegenerative diseases besides Alzheimer's, and could be used for delivering functional imaging probes to the brain for basic research.
Despite promising advances in immunotherapeutics [17,30,31,32,33] and PET imaging [34,35,36], Alzheimer's disease remains intractable to current therapeutic and diagnostic strategies. By overcoming the BBB, FUS-MB may potentiate agents with demonstrated efficacy and could open new avenues of research by reducing the developmental constraints on new Alzheimer's agents. We view this as a powerful preclinical research tool. A clinical FUS system composed of a 512-element phased transducer array at a frequency similar to the current study (670 kHz) has been developed for MRI-guided intracranial thermal ablation [5,37]. We suggest that such a system could feasibly be used at low powers to achieve noninvasive BBB disruption with high spatial resolution for targeted FUS-MB delivery. Before translation to the humans, however, FUS-MB must pass vigorous safety and efficacy benchmarks to prove therapeutic benefit and minimize side-effects. Furthermore, we must better understand the underlying biomechanical mechanisms for FUS-MB therapy. Ongoing work at our institutions is aimed at understanding the cellular response to FUS-MB and demonstrating the therapeutic effects of FUS-MB-enhanced drug delivery for CNS disorders.

MR-guided FUS-MB Treatment
All animal experiments were performed at Brigham and Women's Hospital under national and institutional guidelines using protocols authorized by the Harvard Medical Area Standing Committee on Animals. Experiments were conducted using an MR-compatible sonication system consisting of an ultrasound transducer submerged in a small tank of degassed, deionized water coupled to an animal platform above (see Figure 1). The transducer was attached to a manual XYZ positioning system to allow fine positioning of the focal volume. Sonication pressure and power were estimated from calibration measurements described previously [6]. The entire sonication system was placed on the MR table and moved into a clinical 3T MR scanner (GE Healthcare, Milwaukee, WI) for imaging. We determined focal volume coordinates by MR thermometry in a gel phantom sonicated at high powers [38].

Benchtop FUS-MB Treatment
Similar to above, the transducer was mounted on an XYZ positioning system and submerged in a tank of degassed, deionized water. Ultrasound energy was coupled to the animal above via a mylar window. Before experiments, the transducer was focused onto cross-hairs drawn onto the mylar window, providing a reference for animal alignment. Mice were placed supine with the shaved head directly contacting a mylar window and positioned such that the transducer focus was 4 mm caudal to the eyes and centrally aligned; the transducer was then moved 2-3 mm to the right to place the transducer focus squarely in the right hemisphere.
Transgenic mice (n = 8, 9-12 month old, B6C3-Tg(APPswe, PSEN1dE9)85Dbo/J) were anesthetized and prepped for treatment as described above. Of the 8 mice, 5 were treated and included in study, 1 was sham-treated, and 2 were excluded because they died before or during treatment, putatively from anesthesia. We treated the mice as described above with ultrasound exposure and concurrent IV administration of microbubbles. Some mice (n = 3) received rabbit anti-Ab antibodies (0.1 ml of 53 mg/ml whole sera, Sigma Aldrich) IV immediately before FUS-MB. Ten minutes following FUS-MB, mice received Trypan blue or Evans blue (0.06 ml, 4%, IV), which are used interchangeably for BBB studies. The mice were perfused 3-6 hrs after FUS-MB as above.

Tissue Analysis
Extracted brains were photographed (FinePix S7000, Fuji-film, Japan) and imaged using a multi-spectral small animal imaging system (Maestro, CRI, Woburn, MA, USA) to confirm Trypan blue staining. Brains were sectioned at 20 or 50 mm and representative sections were stained using standard IHC or H&E. Prepared sections were mounted and imaged using multiphoton microscopy (Bio-Rad Laboratories, Hercules, CA, USA) and a microarray scanner (ScanArray Express, Perkin Elmer, Waltham, MA, USA). Images were analyzed using ImageJ [39]. Fluorescence fold changes were calculated by comparing the mean fluorescence in large ROIs from treated and contralateral untreated cortex. Fluorescence counts were normalized to mean signal from untreated thalamus to reduce the artifact from background staining and autofluorescence (so-called Fo in Figure 3).  Figure S3 Benchtop focused ultrasound-microbubble treatment. Transgenic mice were sonicated at a single location in the right hemisphere using a benchtop sonication system and received Trypan blue or Evans blue IV. 5/5 animals exhibited focal blue staining in the right hemisphere. (a) Focal blue staining from Trypan blue on post-mortem, excised brain. (b) Schematic displaying sonication locations (red and blue dotted lines) from two different system users (red vs. blue) for two intended target locations (red and blue crosses). Scale bar: B, 1 cm. Found at: doi:10.1371/journal.pone.0002175.s003 (5.14 MB TIF)


extfieldsofstudy: Biology Medicine

s2fieldsofstudy: Biology Medicine

text:
Multidimensional Proteomics Analysis of Amniotic Fluid to Provide Insight into the Mechanisms of Idiopathic Preterm Birth

Background Though recent advancement in proteomics has provided a novel perspective on several distinct pathogenetic mechanisms leading to preterm birth (inflammation, bleeding), the etiology of most preterm births still remains elusive. We conducted a multidimensional proteomic analysis of the amniotic fluid to identify pathways related to preterm birth in the absence of inflammation or bleeding. Methodology/Principal Findings A proteomic fingerprint was generated from fresh amniotic fluid using surface-enhanced laser desorbtion ionization time of flight (SELDI-TOF) mass spectrometry in a total of 286 consecutive samples retrieved from women who presented with signs or symptoms of preterm labor or preterm premature rupture of the membranes. Inflammation and/or bleeding proteomic patterns were detected in 32% (92/286) of the SELDI tracings. In the remaining tracings, a hierarchical algorithm was applied based on descriptors quantifying similarity/dissimilarity among proteomic fingerprints. This allowed identification of a novel profile (Q-profile) based on the presence of 5 SELDI peaks in the 10–12.5 kDa mass area. Women displaying the Q-profile (mean±SD, gestational age: 25±4 weeks, n = 40) were more likely to deliver preterm despite expectant management in the context of intact membranes and normal amniotic fluid clinical results. Utilizing identification-centered proteomics techniques (fluorescence two-dimensional differential gel electrophoresis, robotic tryptic digestion and mass spectrometry) coupled with Protein ANalysis THrough Evolutionary Relationships (PANTHER) ontological classifications, we determined that in amniotic fluids with Q-profile the differentially expressed proteins are primarily involved in non-inflammatory biological processes such as protein metabolism, signal transduction and transport. Conclusion/Significance Proteomic profiling of amniotic fluid coupled with non-hierarchical bioinformatics algorithms identified a subgroup of patients at risk for preterm birth in the absence of intra-amniotic inflammation or bleeding, suggesting a novel pathogenetic pathway leading to preterm birth. The altered proteins may offer opportunities for therapeutical intervention and future drug development to prevent prematurity.

Introduction
Prematurity remains the leading cause of early neonatal deaths and long term neuro-developmental handicap [1,2]. Despite extensive research and a variety of interventions and preventive strategies, the rate of preterm birth has steadily increased over the past 20 years to reach a high of 12.8 percent (%) in 2005 [3]. Several pathophysiological mechanisms have been implicated in triggering preterm birth [4]. Of these, genetic predisposition, fetal stress, excessive stretching, inflammation/ infection and decidual hemorrhage have received increased attention. [4] In prior studies we concentrated our attention on investigating the changes in amniotic fluid proteome in the context of inflammation and decidual hemorrhage induced-preterm birth [5,6] We devised a novel, stepwise strategy to extract relevant proteomic biomarkers based on filter preferences applied sequentially. This strategy was named ''Mass restricted scoring or MR scoring'' [5]. Based on these principles we defined relevant disease biomarker patterns characteristic for intra-amniotic inflammation and decidual bleeding [5,6]. Proteomic identification techniques recognized the four protein biomarkers (which comprise the proteomic profile or MR score characteristic for intra-amniotic inflammation) as neutrophil defensin-2, neutrophil defensin-1, calgranulin C and calgranulin A. A combination of peaks corresponding to free hemoglobin (Hb) chains was component of the proteomic pattern characteristic for decidual hemorrhage [6].
In subsequent studies the presence of an inflammatory proteomic pattern in amniotic fluid proved to be predictive not only for preterm birth but also for histological funisitis and early onset neonatal sepsis [7,8]. Regrettably, notwithstanding recent advancements in comprehending the pathophysiology of preterm birth, the etiology of this syndrome still remains unknown [9].
The transition from the past era of hypothesis-driven research to that of a discovery-determined one stemmed from opportunities presented by the contemporaneous revolution in biomedical sciences such as the one recently witnessed in proteomics [10]. Proteomics seems to have all the attributes necessary to provide the needed breakthrough in understanding the physiopathology of idiopathic preterm birth and to identify novel inroads to help prevent its occurrence. To that end we expect that causes other that inflammation and bleeding would also generate aberrant amniotic fluid protein expression levels that translate in characteristic proteomic signatures related to the underlying cause. The objective of our study was to identify biomarker patterns relating to preterm birth in the absence of inflammation or bleeding and provide a comprehensive analysis of the amniotic fluid proteome in cases with such abnormal patterns.

Study population
We conducted a prospective study that included 285 women recruited following admission to the Labor and Birth unit or High Risk antepartum units at Yale New Haven Hospital (YNHH). Symptoms of preterm labor, preterm premature rupture of membranes (PPROM), advanced cervical dilatation ($3 centimeters) and/or uterine contractions unresponsive to tocolysis were considered clinical indications for ultrasound-guided amniocentesis to rule-out infection. Women were enrolled consecutively based on a protocol approved by the Human Investigation Committee of Yale University. Written informed consent for the use of biological samples was obtained prior to the procedure. All women in this study had a singleton fetus at ,37 weeks gestational age. Exclusion criteria included: viral infections (human immunodeficiency virus or hepatitis), anhydramnios and presence of fetal heart rate abnormalities (bradycardia, recurrent variable and late decelerations) requiring emergency intervention. Gestational age was established based on a second trimester ultrasonographic examination prior to 20 weeks in all instances. We defined preterm labor as presence of regular uterine contractions accompanied by cervical effacement and/or dilatation in patients ,37 weeks gestational age. The diagnosis of PPROM was confirmed by vaginal amniotic fluid ''pooling'', ''nitrazine'', ''ferning'' or by an amniocentesis-dye positive test. Clinical chorioamnionitis was suspected in the presence of either fever .38uCelsius (C), uterine tenderness or fetal tachycardia. The decision to perform an amniocentesis and patient management including the decision to deliver the fetus were at the discretion of the clinical team independent of our study protocol. Patients received steroids for enhancement of fetal lung maturity if gestational age was between 24 and 32 weeks. Antibiotic therapy was initiated as per the usual PPROM protocol (ampicillin and erythromycin i.v. in the first 48 hours, followed thereafter by oral medication) [11]. After amniocentesis, all participants were followed prospectively to the point of delivery. We recorded whether preterm delivery of the fetus occurred subsequent to spontaneous onset of uterine contractions or secondary to a clinically indicated delivery aimed to deliver the fetus in the absence of uterine contractions (including gestational age .34 weeks, evidence of intra-amniotic infection, prolapsed cord, non-reassuring fetal heart rate) [12].
Following retrieval, amniotic fluid was cultured for aerobic and anaerobic bacteria, Ureaplasma and Mycoplasma species by the YNHH microbiology laboratory. The clinical laboratory tests performed for the purpose of diagnosing inflammation/infection were: glucose, lactate dehydrogenase (LDH), Gram stain, white (WBC) and red blood cell (RBC) counts. Research samples were spun at 30006g at 4uC for 20 min., aliquoted and stored at -80uC for further study.
Proteomic profiling for identification of patterns of intraamniotic inflammation (MR score) and bleeding We used surface-enhanced laser desorbtion ionization time-offlight (SELDI-TOF) mass spectrometry to screen for the presence of biomarkers characteristic of inflammation and bleeding in fresh samples of amniotic fluid. The results were not made available to the clinical management team. The methodology for generation of the MR score and identification of bleeding biomarkers has been previously described [5,6]. Briefly, 5 microliters (mL) of 10-fold diluted amniotic fluid in phosphate-buffered saline solution (PBS) was placed on spots of duplicate H4 arrays (8-spot H4 array; Ciphergen Biosystems). After 1-hour incubation, the sample was aspirated, and the spots individually washed with 20% aqueous acetonitrile solution, air-dried, and covered with energy absorbing molecule (EAM) matrix solution of either 1-mL of 20% saturated a-cyano-4-hydroxycinnamic acid (CHCA) in 0.5% trifluoroacetic acid/50% acetonitrile on one array, or 2 sequential applications of 1-mL 50% saturated solution (in 0.5% trifluoroacetic acid/50% acetonitrile) of sinapinic acid (SPA) on the other. The proteomic arrays were read in the the ProteinChip Reader (model PBS II; Ciphergen Biosystems) using the ProteinChip software (version 3.0; Ciphergen Biosystems). For identification of proteomic profiles characteristic for inflammation (the MR score) and bleeding (Hb peaks), examination of SELDI tracings was targeted in 3 mass ranges: 3000 to 4000 Daltons (Da) (CHCA), 10 to 12.5 kiloDaltons (kDa) (SPA), 14 to 17 kDa (SPA). The data for the biomarkers of the MR score were extracted from the 3000 to 4000 Da (P1 and P2, Figure 1A) and 10 to 12.5 kDa (P3 and P4, Figure 1B) ranges. The data for the Hb peaks were extracted from the 14 to 17 kDa region ( Figure 1C). Both the MR score and Hb profile provide qualitative information regarding the presence or absence of intra-amniotic inflammation or bleeding, respectively [5,6]. A categorical value of 1 is assigned if a biomarker peak is present and 0 if absent. Thus, the MR score (inflammation) ranges from 0 to 4, depending upon the presence or absence of each of the four protein biomarkers and is a measure of the severity of intra-amniotic inflammation. MR scores 3-4 are indicative of severe inflammation. The Hb score (ranging from 0-2 depending with the number of free Hb chains identified) indicates presence of intra-amniotic bleeding for any value different than 0 [6]. The generation of the MR and Hb scores for each SELDI tracing was performed by an investigator who was blinded to the origins of the amniotic fluid sample, clinical presentation, clinical outcome or results of the placental histological examination.
Extraction of the proteomic pattern characteristic of idiopathic preterm birth A novel computational algorithm was employed to analyze the proteomic information between 500 to 300,000 Da in our experimental conditions. From a bioinformatics perspective, SELDI tracings display silent areas alternating with hypervariable areas. Silent areas are mass ranges devoid of proteomic information and are identified by their similarity with the tracings obtained with the PBS diluent alone. In contrast, hypervariable areas are the mass ranges rich in potential biomarker peaks and are delineated by the high level of dissimilarity from the PBS tracings. For the purpose of reducing complexity, our algorithm first eliminated silent areas while extracting hypervariable areas. We next performed a hierarchical clustering of the tracings based on their similarity/dissimilarity in peaks present in hypervariable areas. Presence or absence of peaks was converted to binary strings with equal number of attributes and a hierarchical clustering algorithm applied. From the several hypervariable areas analyzed, we focused our attention on the mass area between 10 to 12.5 kDa due to the non-random clustering of the tracings based on a complex of five peaks in this region which we named the Qprofile (Figure 2A). The peak components are labeled Q1-Q5.
Fluorescence two-dimensional differential gel electrophoresis (2D-DIGE) To clarify and expand the biological significance of the Qprofile on a larger scale we employed 2D-DIGE on select pairs of amniotic fluid samples matched for maternal age, parity, gestational age at amniocentesis and membrane status. We used amniotic fluid samples selected from patients with the Q-profile who delivered preterm (n = 3) and compared the results with samples of amniotic fluid retrieved from women who presented with symptoms of preterm labor but delivered at term (CRL, n = 3). 2D-DIGE was performed using the Ettan DIGE system (GE Healthcare). Amniotic fluid proteins (67 mg) were labeled with either Cy3 (Q-profile) or Cy5 (CRL). A reference pool (25 mg total protein) was labeled with Cy2 and used as internal control. For each pair, labeled samples were pooled and isoelectric focusing was performed using an isoelectric point range of 3-10. SDS-PAGE on a 12% gel was performed for the second dimension. After spot detection, automatic background correction, spot volume normalization and volume ratio calculation, dye ratios were determined using DeCyder software (GE Healthcare). Spots corresponding to $1.5-fold changes [13] were robotically excised using the Ettan Spot Picker instrument (GE Healthcare) and subjected to automated in-gel tryptic digestion on the Ettan TA Digester (GE Healthcare). Automated MALDI-MS/MS spectra were acquired on the 4800 TOF/TOF proteomics analyzer (Applied Biosystems, Foster City, CA) and the resulting peptide . The x-axis of the tracings represents the molecular weight in Daltons; the y-axis represents the normalized peak intensity. R denotes a reference protein peak present in all fluid samples which corresponds to a fragment of beta-2 microglobulin. doi:10.1371/journal.pone.0002049.g001 masses subjected to database searching using Mascot algorithms (Matrix Science, Boston, MA). The remaining aliquots of digests of protein spots that were not identified by this approach were subjected to LC/MS/MS analysis (Micromass Q-TOF) followed by Sequest (Thermo Finnegan, San Jose CA) database searches.

Pathway analysis
Differentially expressed proteins were classified based on the PANTHER (Protein ANalysis THrough Evolutionary Relationships) system (http://www.pantherdb.org) [14]. PANTHER is a unique resource that classifies genes and proteins by their functions, using published scientific experimental evidence and evolutionary relationships abstracted by curators with the goal of predicting function even in the absence of direct experimental evidence. Proteins are classified into families and subfamilies of shared function, which are then categorized using a highly controlled vocabulary (ontology terms) by biological process, molecular function and molecular pathway.

Histological evaluation of the placenta
The diagnosis of histological chorioamnionitis was based on neutrophil infiltration of the chorionic plate, amniochorionic membranes and chorio-decidua. Three histological stages of chorioamnionitis [15] (stage I: intervillositis, stage II: chorionic inflammation, and stage III: full-thickness inflammation of both chorion and amnion) were complemented by the histological grading system devised by Salafia et. al. which includes four grades of inflammation of the amnion, chorion-decidua and funistis [16]. Presence of hemosiderin in the placental basal plate, extraplacental membranes and decidua confirmed by iron stains was suggestive of bleeding [17].

Statistical analysis
Data is presented as mean and standard deviation (SD) or, if non-normally distributed, as median and range. Statistical analyses were performed with Sigma Stat, version 2.03 (SPSS Inc., Chicago, IL). Data was compared with one-way ANOVA followed by Student Newman Keuls tests (parametric) or Kruskal-Wallis on ranks followed by Dunn's tests (non-parametric), to adjust for multiple comparisons as appropriate. Comparisons between proportions were done with Chi-square or Fisher's exact tests. A p value of ,0.05 was considered significant.

Results
Description of a novel proteomic pattern (Q-profile) associated with preterm birth in the absence of inflammation and bleeding Based on the established proteomic fingerprints of inflammation and bleeding, we identified that of the 285 SELDI tracings screened, 92 (32%) had either evidence of intra-amniotic inflammation (n = 71, MR 3-4), bleeding (n = 6, Hb peaks present) or both (n = 15) ( Figure 3). Application of the hierarchical Clinical and outcome characteristics of women displaying the novel proteomic pattern (Q-profile) The cases displaying any type of amniotic fluid proteomic fingerprint (n = 124) had a significantly higher frequency of preterm delivery (88%) compared to the rest (65%, p,0.001). To further understand the biological significance of the Q-profile we analyzed the cases that exclusively displayed this profile (n = 32) in comparison to four additional groups: those displaying the inflammation fingerprint alone (n = 64), the bleeding fingerprint alone (n = 6), the combined inflammation and bleeding profile (n = 14) and those displaying no proteomic profile who delivered at term (n = 33). In Table 1 we present the demographical and outcome characteristics of these 5 groups of women. As mentioned in Methods section, all women had clinical signs and symptoms of preterm birth at the time of amniocentesis. Women displaying either the Q-profile or the inflammation profile were at an earlier gestational age at time of amniocentesis compared to the reference group (ANOVA, p#0.001), but there were no differences among the 4 groups that displayed any proteomic profile. Women with the Q-profile presented more often with intact membranes compared to the inflammation and/ or bleeding groups. Among the groups with abnormal proteomic profiles, there was no significant difference in gestational age at birth. However, a significantly higher proportion of women displaying the bleeding and/or inflammation profile, underwent an indicated preterm delivery subsequent to amniocentesis results. Therefore, despite their expectant management, 84% (27/32) of the women with the still Q-profile delivered prematurely. The amniocentesis-to-delivery interval was longer for women displaying the Q-profile as compared to those displaying the proteomic fingerprint characteristic for inflammation and/or bleeding. Moreover, newborns in the inflammation groups (with or without bleeding), but not those with the Qprofile had significantly lower Apgar scores compared to all other groups.
In Table 2 we present the results of the amniotic fluid analysis for the 5 different groups. We determined that the Q-profile group is characterized by amniotic fluid results similar to the group with no profile and delivery at term. 8 All women displaying the Qprofile had negative Gram stain, negative culture results and normal levels of inflammatory cytokines. This was determined to be in sharp contrast to the women who had the inflammation profile present (with or without bleeding) who had significantly lower glucose and elevated LDH activity, WBC, RBC counts and elevated levels of IL-6 and MMP-8. The incidence of a positive Gram stain and/or amniotic fluid culture result was also significantly higher in the inflammation groups. The bleeding alone group was characterized by elevated LDH activity levels in the context of a higher RBC (but not WBC) count, and normal IL-6 and MMP-8 levels.
The results of the pathologic examination of the placenta were available in 101 of the cases in the 5 groups (Table 3). Overall, the prevalence of histological chorioamnionitis (stages II-III) was 65% (65/101). Similar to the group of women who delivered at term, the women with the Q-profile had virtually no evidence of chorioamnionitis, amnionitis or funisitis, despite delivering preterm. Lastly, bleeding was more frequently confirmed histologically in the context of bleeding-induced preterm birth (with and without inflammation).
Based on these results we conclude that women displaying the amniotic fluid Q-profile present most often with intact membranes and are managed expectantly in the context of normal amniotic fluid results. Yet, this group has a significantly higher risk for preterm birth which typically occurs in the context of normal placental histology. These findings concur with the concept that the Q-profile is representative of an amniotic fluid proteomic fingerprint which is different from that of the women with intraamniotic inflammation or bleeding. No clinical or biochemical tests other than proteomic profiling of the amniotic fluid was able to delineate this high-risk group of women.
Characterization of the amniotic fluid proteome representative of women displaying the novel profile (Qprofile) Figure 4 shows a representative 2D-DIGE image of proteins differentially expressed (red: spots up-regulated $1.5-fold, blue: spots down-regulated$1.5-fold) between samples displaying the Q-profile and reference samples amniotic fluid samples (matched for gestational age at time of amniocentesis). Interestingly, more spots (n = 60) appeared down-regulated in the amniotic fluids displaying the Q-profile rather than up-regulated (n = 28). Of the spots picked for identification, 22 matched to a protein in the database with more than one peptide. Few spots matched to the same NCBI GenID transcript number. In fact, a total of 17 distinct protein database matches appeared and were differentially regulated at least 1.5-fold (Table 4) among the 2 groups. Further data mining using the PANTHER classification system revealed that of the unambiguous identities, 10 protein products belonged to 7 distinct and well-classified biological processes. The most populated was protein metabolism (5/10 transcripts matched) and signal transduction (3/10 transcripts matched) ( Figure 5). PAN-THER further converged the aberrantly expressed identities by matching several transcripts to the same protein precursor in the database (Table 5). This allowed us to conclude that the amniotic fluids with Q-profile are characterized by up-regulation in insulin growth factor binding protein (IGFBP-1), apolipoproteins (APO A-I and APO A-IV), lumican and bikunin (alpha-1-microglobulin light chain protein, complex-forming glycoprotein heterogeneous in charge) and down-regulation in the anti-proteolytic factors alpha-1-antitrypsin (SERPINA1), alpha-2-antiplasmin (SEPR-PINF1, pigment epithelium derived factor) ( Table 5).

Discussion
Employing multidimensional proteomics analysis of amniotic fluid coupled with bioinformatic algorithms we were able to identify a novel subgroup of patients at risk for preterm birth that share common clinical, biochemical and proteomic features. These cases were distinct from those characterized by intraamniotic inflammation or bleeding. The findings of the present study further support the notion that preterm birth is the result of several distinct pathogenetic pathways for which increased uterine contractility, cervical ripening and/or PPROM are simply common clinical features of the end product.
Proteomics encompasses protein separation and identification techniques along with the necessary bioinformatic tools that allow for biomarker discovery. Two current and rather opposing proteomic approaches have emerged. One relies on generating proteomic patterns from biological samples using high-throughput mass spectrometry approaches while minimizing the need to know the identity of the discriminatory biomarkers (proteomic pattern diagnostic approach) [18]. The other focuses on identification of proteins by digesting them into peptides and sequencing them using tandem mass spectrometry and database searching (identification-centered proteomic approach) [19]. In several prior studies, we and others have mined the amniotic fluid proteome for combinations of biomarkers that would separate pathogenetic pathways leading to preterm birth and could potentially be used as predictors of outcome [10] In the present study we used a multidimensional proteomic approach that combined SELDIbased diagnostic pattern proteomics with identification-based techniques (2D-DIGE) to find and understand changes in amniotic fluid proteome associated with spontaneous preterm birth in the absence of inflammation and bleeding. While in all prior studies [5,6,10] the relevant proteomic profiles were derived by comparing a ''diseased set'' versus a ''normal set'' as defined by other non-proteomic clinical, biochemical or microbiological criteria (differential proteomics), in the present study we used a reverse approach. Since none of the current tools available in clinical practice were of use to discriminate our patient pool despite significant differences in their pregnancy outcomes we sought to define a novel clinical instrument. After we eliminated the proteomic tracings characterized by diagnostic patterns indicative of the presence of inflammatory and bleeding, among the remaining cases we found a distinct cluster of patients characterized by presence of 5 SELDI peaks in the 10-12.5 kDa region (Q-profile). By exploring the common clinical characteristics of this subgroup we determined that these women presented most often with preterm labor and intact membranes in the context of normal amniotic fluid clinical and microbiological results. However, the majority of cases in this subgroup delivered prematurely despite tocolytic and expectant management per standard protocols. To our knowledge the Q-profile is the first proteomic pattern with the ability to identify women at risk for  preterm birth in the absence of intra-amniotic inflammation and bleeding.
With few exceptions, the current stage of medical practice emphasizes disease classification based on shared clinical features which together are representative of a syndrome. Assignment of individual cases to these diagnostic categories frequently dictates treatment choices. However, while in clinical instances such as tumors or infectious etiologies the medical treatment is further conditioned based on strict histopathologic or microbiologic examinations, obstetrical practice lags seriously behind in this respect. Perhaps, our failure to recognize and prevent preterm birth rests with the inability to rapidly and accurately discern  among the several distinct pathogenetic mechanisms leading to premature delivery. Personalized medicine, stands at the opposite end of this ''one size fits all'' approach and relies heavily on development of classification algorithms based on subclinical features and biomarkers [20]. Nowhere is a ''pathway specific targeted treatment approach'' more needed than in preventing premature delivery and its consequences to the newborn baby. To further identify novel pathways leading to preterm birth which may allow for individualized targeted treatment we proceeded with a proteomic identification-centered approach using the classical differential proteomics paradigm by defining the ''disease set'' as the samples expressing the Q-profile in the context of a preterm delivery. In the current study we have determined that the differentially expressed proteins which characterize the Q-profile are primarily involved in protein metabolism, signal transduction and transport. The identities of the proteins matched in the database pointed to a metabolic rather than inflammatory abnormality since none of the protein identities had apparent inflammatory function. APO A-I and APO A-IV are associated with high density lipoprotein (HDL) and chylomicrons and have important roles in cholesterol, trygliceride and phospholipid transport and metabolism [21]. Most recently it has been proposed that APO A-IV is a satiety factor acting centrally in the brain. In fasted young animals there is a dramatic increase in liver mRNA synthesis and serum levels of APO A-I and A-IV in response to glucocorticoids and insulin increased by fasting stress [22]. APO A-I and A-IV have been described previously in amniotic fluid, but to our knowledge there has been no study investigating their levels or phenotypic variants as it relates to the fetus and to preterm birth [23].
IGFBP-1 is the predominant insulin-like growth factor binding protein in amniotic fluid [24]. It is produced by the decidua and fetal liver and it is thought to play an important role in fetal growth [25]. Higher mid-pregnancy levels of IGFBP-1 have been associated with growth failure [26]. Using an in vitro model Popovici et al demonstrated that hypoxia up-regulates fetal hepatocyte IGFBP-1 mRNA steady-state levels and protein, which is the major IGFBP derived from the fetal hepatocytes [27]. Another protein found differentially abundant in samples with the Q-profile was lumican, a member of the small leucine-rich proteoglycan family (SLRP) with important roles in embryonic development, tissue repair, tumor growth, organization of collagens [28] and maintenance of corneal transparency [29].
Bikunin (also known as the ''complex-forming glycoprotein heterogeneous in charge'') is a Kunitz-type serin-protease inhibitor predominantly found in amniotic fluid and to a lesser degree in urine and serum where it circulates bound to the inter-alpha inhibitor proteins [30] A number of studies have shown that many serine protease inhibitors have complex effects on cellular growth and differentiation unrelated to their anti-proteolytic function [31] Bikunin in particular has the ability to modulate cell growth and to block cellular calcium uptake and is currently being evaluated as a novel anti-metastatic agent [32,33]. Additionally, bikunin has a significant anti-inflammatory function by blocking systemic cytokine induction in response to endotoxin challenge in vivo [34] Finally, bikunin inhibits entotoxin-induced preterm labor in mice, [35] inhibits prostaglandin expression from amnion cells [36] suppresses uterine contractions [37] by reducing calcium influx in myometrial cells [38] and suppresses premature cervical ripening [39] which are all key pathogenetic processes necessary for preterm birth. The observed upregulation in bikunin levels may reflect an endogenous compensatory mechanism aimed to prolong pregnancy and downplay inflammatory processes. This may explain the longer duration to delivery in women with Q profile compared to the other groups exhibiting abnormal proteomic profiles, despite the similar gestational age at amniocentesis.
All proteins found differentially down-regulated in amniotic fluid expressing the Q-profile (pigment epithelium-derived factor [PEDF] and alpha-1 antitrypsin) were members of serpin superfamily. The roles of these factors in amniotic fluid are not well understood. PEDF is known to be the most potent inhibitor of angiogenesis in the mammalian ocular compartment [40]. It also has neurotrophic activity, both in the retina and in the central nervous system, and is highly up-regulated in young versus senescent fibroblasts [40]. No prior study investigated the presence PEDF in amniotic fluid or defined ''normal levels''. One study found higher levels of trypsin activity and lower alpha 1-antitrypsin concentration in amniotic fluid of women with PPROM [41] but a subsequent study found no difference between amniotic fluid alpha-1 antitrypsin in premature birth with intact versus ruptured membranes or in those with intra-amniotic infection [42]. The present study demonstrates how diagnostic pattern proteomics can be used in conjunction with identification-centered proteomic techniques and bioinformatic tools to provide insight into a problem which has frustrated clinicians for decades. We acknowledge that at this time due to the limitation of the current technology we have not been able to identify the specific proteins responsible for the five SELDI peaks of the Q-profile. Furthermore as SELDI and 2D-DIGE technologies are not equivalent, the biomarker peaks comprising the Q profile may not be part of the differentially expressed identities. Yet the SELDI diagnostic pattern itself proved essential in isolating a subgroup of preterm birth cases with unique features at the level of their amniotic fluid proteome. This will perhaps prompt a future research direction and paradigm into the prevention of prematurity.

Author Contributions
Conceived and designed the experiments: CB IB. Performed the experiments: GZ. IB. Analyzed the data: CB IB. Contributed reagents/ materials/analysis tools: CB IB. Wrote the paper: CB IB. Other: Critically revised the manuscript: GZ. Participated in recruitment of the patients in a prospective manner, collection of biological specimens and participated in writing of the report: SA ST VR VR.


text:
An Unbiased Systems Genetics Approach to Mapping Genetic Loci Modulating Susceptibility to Severe Streptococcal Sepsis

Striking individual differences in severity of group A streptococcal (GAS) sepsis have been noted, even among patients infected with the same bacterial strain. We had provided evidence that HLA class II allelic variation contributes significantly to differences in systemic disease severity by modulating host responses to streptococcal superantigens. Inasmuch as the bacteria produce additional virulence factors that participate in the pathogenesis of this complex disease, we sought to identify additional gene networks modulating GAS sepsis. Accordingly, we applied a systems genetics approach using a panel of advanced recombinant inbred mice. By analyzing disease phenotypes in the context of mice genotypes we identified a highly significant quantitative trait locus (QTL) on Chromosome 2 between 22 and 34 Mb that strongly predicts disease severity, accounting for 25%–30% of variance. This QTL harbors several polymorphic genes known to regulate immune responses to bacterial infections. We evaluated candidate genes within this QTL using multiple parameters that included linkage, gene ontology, variation in gene expression, cocitation networks, and biological relevance, and identified interleukin1 alpha and prostaglandin E synthases pathways as key networks involved in modulating GAS sepsis severity. The association of GAS sepsis with multiple pathways underscores the complexity of traits modulating GAS sepsis and provides a powerful approach for analyzing interactive traits affecting outcomes of other infectious diseases.

Introduction
Infectious diseases, like most human diseases, are modulated by complex traits. Susceptibility and clinical outcomes of infections are often a manifestation of interactions between the host's complex traits and the pathogen's virulence components. Identification of genes and molecular networks that influence host responses to infectious agents can provide a disease road map that would focus the discovery of effective diagnostics and therapeutics.
Group A streptococci (GAS) are classified on the basis of surface M protein antigens into more than 100 serotypes, but recent studies showed a high degree of diversification within a serotype that is driven primarily by horizontal gene transfer [1][2][3]. It is widely believed that such events are responsible for the emergence of highly virulent strains in the 1980's, including a hypervirulent M1T1 clonal strain, coinciding with the resurgence of severe invasive GAS infections associated with streptococcal toxic shock syndrome (STSS) and necrotizing fasciitis (NF), also known as the ''flesh eating'' disease [4]. We showed that this hypervirulent M1T1 clinical strain can cause sore throat or mild bacteremia and erysipelas in some patients, while in others it can cause STSS and NF [5]. These findings suggested a strong role for host factors in modulating the outcome of infection by this highly virulent strain. Indeed, we found that allelic variations in host HLA class II haplotypes are associated with markedly different outcomes of GAS sepsis in humans [6,7]. We confirmed these associations using HLA transgenic mice that were expressing different HLA class II alleles [8]. Inasmuch as STSS pathogenesis is largely mediated by streptococcal superantigens (Strep SAgs), and because HLA class II molecules serve as SAg receptors, such an association was expected. However, GAS is rich in many other virulence factors, and we believe that some of those virulence factors, beside SAgs, may also interact with additional host factors to modulate host responses to GAS infections.
To identify additional host factors involved in GAS severity we needed an approach that allowed us to uncover the effect of interactions between complex, polymorphic genetic traits that may modulate sepsis outcomes. Previous animal models for GAS sepsis included various regular conventional inbred mouse strains [9,10], congenic strains [11], and HLA transgenic mice [8,12]. These models confirmed that host genetic variability can have a strong effect on infection outcome. Despite their significant utility, these mouse models do not offer the genetic diversity that is characteristic of the human population.
In this study, we used a panel of advanced recombinant inbred (ARI) mice as a genetically diverse, segregating reference population that affords a powerful tool for systems genetics approaches. Recombinant inbred (RI) strains have been successfully used to map quantitative trait loci (QTLs) associated with various phenotypes and diseases [13][14][15][16][17][18][19][20]. We used the BXD panel of ARI mice derived from C57BL/6J (B6) and DBA/2J (D2) strains and consisting of homozygous, inbred BXD lines, each of which is genetically distinct. Using this genetically diverse BXD panel, we mapped QTLs modulating GAS sepsis severity, and identified candidate genes within these QTLs that were parsed into pathways likely to modulate the severity of this complex infectious disease.

Results
Variable susceptibility to severe GAS sepsis in genetically distinct mice Our initial studies [20] demonstrated that there is considerable variability among BXD strains with respect to susceptibility to severe GAS sepsis. To quantify differences among strains in this study, we used three main quantitative traits, namely animal survival, bacteremia, and bacterial dissemination to spleen.
We infected mice (n = 5-26 mice per strain), belonging to 32 strains (30 different BXD strains and their parental strains, B6 and D2), intravenously (i.v.) with the same bacterial dose (261.5610 7 CFU/mouse). Survival was expressed as normalized corrected relative survival indices (cRSI) calculated for each of the 32 strains as detailed in materials and methods. We observed significant difference in relative susceptibility to GAS sepsis across the BXD panel (P#0.0001), Figure 1A. This wide range of susceptibility across the various strains, together with the finding that some of the strains showed phenotypes considerably more exaggerated than the parental strains (B6 and D2), is an illustration of how different combination of polymorphic genes can manifest quite differently according to the overall genetic context.
Similarly, we determined bacterial load in blood (log CFU/ml blood), 24 h post-infection and found considerable variation across the strains. In general there was an inverse correlation between mice survival and extent of bacteremia (r = 20.471, P#0.0001, R 2 = 22.2%), Figure 1B. Bacterial dissemination to spleen also varied across the strains, showing a stronger inverse correlation with mice survival (r = 20.717, P#0.0001, R 2 = 51.4%), Figure 1C.
Although these inverse correlations between mice survival and bacterial load in blood and spleen made biological sense because it is anticipated that susceptible strains would have higher bacterial load than in resistant strains, there were several exceptions. For example, although strains BXD44, and BXD45 are ranked susceptible based on their survival, they showed low bacterial load in blood and spleen. Similarly, BXD43 and BXD85 strains, which are ranked resistant, had a high bacterial load in their spleen. Another interesting observation was that, in general, susceptible strains survival was better correlated with bacterial load in blood and spleen than resistant strains. Together, these findings confirm that there is more than one mechanism modulating differential susceptibility or resistance to severe GAS sepsis [20].

Genome-Wide Scans for Mapping GAS Susceptibility QTLs
We used bioinformatics tools available through Gene Network (GN) to link measured phenotypes to strains genotypes. Each of the quantified phenotypes was analyzed in the context of the studied mice genotypes and single nucleotide polymorphism (SNPs) using 3795 SNPs and microsatellite markers for BXD strains. Significant QTLs modulating survival, bacteremia 24 h post-infection, and bacterial dissemination to spleen mapped to mouse Chromosome (Chr) 2.
The strongest QTL modulating mouse survival (cRSI) mapped to mouse Chr 2 between 22-34 Mb, with an likelihood ratio statistic (LRS) of 34.2 (P#0.0000001), Figure 2A. A second less significant QTL was also mapped on the same chromosome between 125-150 Mb with an LRS of 12 (P#0.001), and a third QTL on Chr X, Figure 2A. The QTLs for bacteremia and bacterial dissemination to spleen overlapped with those for survival, with slight difference in significance. The first QTL modulating bacteremia mapped to Chr 2 between 22-34 Mb with an LRS of 24.5 (P#0.00001), Figure 2B. The second QTL mapped to the same chromosome between 125-150 Mb with an LRS of 17 (P#0.0001), Figure 2B. A QTL modulating bacterial dissemination to spleen also mapped to Chr 2 between 125-150 Mb with LRS of 15 (P#0.001), Figure 2C.
To further investigate and narrow down the mapped region on Chr 2, we resorted to haplotype maps of BXD strains to select additional strains that may be informative in validating, and further confirming mapped QTLs.
In silico prediction of BXD susceptibility to severe GAS sepsis Our mapped QTLs directed us to select more strains for survival experiments based on differences in their haplotypes

Author Summary
Group A streptococci (GAS) cause a wide variety of human diseases ranging from mild pharyngitis to streptococcal toxic shock syndrome and necrotizing faciitis. Our previous studies have shown that host immunogenetic variation can dictate the clinical outcome of GAS sepsis. As in most human disease, GAS sepsis is likely to be affected by complex interactions between more than one polymorphic gene. We addressed this issue in our study where we present an approach that allowed us to identify multi genetic factors that likely contribute to sepsis severity. We mapped susceptibility to severe GAS sepsis to quantitative trait loci on Chromosome 2 using a panel of genetically diverse inbred mice. The mapped regions have high single nucleotide polymorphism (SNP) density that harbor genes known to play an important role in innate immune response to bacteria. Several of those genes are differentially expressed between susceptible and resistant strains of mice. Our overall approach of systematic dissection of genetic and molecular basis of host susceptibility is not unique to GAS infections, but can be applied to other infectious diseases to develop better diagnostics, design effective therapeutics and predict disease severity based on a set of genetic and soluble biomarkers.
within the QTLs. For example, we selected BXD100 with a D haplotype at Chr 2 between 22-34 Mb region, Figure 3A, and found it to be susceptible. Similarly, we tested BXD87 with a B haplotype in the same region and this strain as predicted, exhibited a resistant phenotype. To further narrow down the mapped QTLs, we chose strains with breakage in the mapped interval, i.e. with recombination at Chr 2 region 22-34 Mb for further survival testing ( Figure 3A). BXD34 with a B haplotype at Chr 2 between 24-33.15 Mb region, was resistant, while BXD60 with a B haplotype at Chr 2 between 20-23.27 Mb region, was susceptible, Figure 1A and 3A. This narrowed down the relevant susceptibility region to Chr 2 between 23.27-33.15 Mb. Using more strains with recombination at this narrowed region, we found BXD51, 64, and 79 to be quite interesting as they showed intermediate resistance suggesting that genes at this region are candidate modulators of the mapped QTL, Figure 3B. Another interesting strain was BXD94 that we found susceptible to infection, yet has a heterozygous genotyping, suggesting that D allele exhibited dominance, Figure 3B.
Similarly, we inspected haplotypes of studied BXD strains at the second QTL between 125-150Mb ( Figure 3C), the majority of susceptible strains had D alleles, while resistant strains had B alleles with some exceptions, for example BXD77 is a susceptible strain, yet had B alleles. It was interesting to find that the susceptible strain BXD44 had B alleles at this QTL ( Figure 3C), suggesting that observed lower bacterial load in blood compared to other susceptible strains ( Figure 1B) might be modulated by B allele in the second QTL. Meanwhile, BXD48, a resistant strain, has a breakage at 135.9 Mb ( Figure 3C) with D alleles suggesting that its relatively higher bacterial load in blood ( Figure 1B) might be modulated by the second QTL. Collectively, these findings suggest that both loci modulate different observed phenotypes of GAS sepsis severity.

Mining for candidate genes and pathways in mapped loci
Based on our in silico strain selection and QTL validation by experimental assessment, we evaluated candidate genes within the mapped QTLs, taking into account linkage, gene ontology analyses, cocitation networks, and biological relevance. To categorize genes and transcripts in the mapped QTLs into functional pathways, we performed functional analyses using Ingenuity Pathways Analysis (IPA) (www.ingenuity.com). We parsed our genes into 50 functional networks; the most relevant networks involved those associated with immune response, cell signaling, cellular assembly and organization, and lipid metabolism. From these interrelated pathways, we selected 37 representative genes to study their role in GAS susceptibility QTL by quantitative analysis of their expression levels in selected resistant and susceptible strains (groups) pre-and post-infection (Table S1). Statistical test is two-way ANOVA (with correction to covariates) P#0.0001. Total number of mice is 489 belonging to 32 strains; number of mice used per strain is indicated. (B) Bar chart of GAS bacteremia expressed as coefficient of mean values of corrected log blood CFUs 24 h post injection (corrected log CFU/ml blood). Strains are ordered by their corrected relative survival indices with the parental strains at the two extremes of the X-axis. Error bars represent the standard errors of the means. Statistical test is two-way ANOVA (with correction to covariates) P#0.0001. (C) Bar chart of GAS dissemination to spleen expressed as coefficient of corrected bacterial load in spleen (log CFU/spleen). Strains are ordered by their corrected relative survival indices with the parental strains at the two extremes of the X-axis. Error bars represent the standard errors of the means. Statistical test is twoway ANOVA (with correction to covariates) P#0.0001. doi:10.1371/journal.ppat.1000042.g001 We found that 28 out of a representative 37 genes were differentially expressed post-infection (Table S2), of those 14 genes were down regulated in resistant group while up regulated in susceptible group post-infection. Nine genes were down regulated in both groups post-infection, and five were up regulated in both groups post-infection (Table S2). Fourteen of the 28 differentially expressed genes showed significant change post-infection in both resistant and susceptible strains (Table 1 and Figure 4). In general, susceptible strains showed increase in the relative expression levels post-infection (19 genes) of both pro and anti-inflammatory genes, e.g. interleukin1 alpha (Il1 a), and Il1 receptor antagonist (Il1rn) respectively. By contrast, in the resistant BXD strains, most of the tested genes showed a decrease (23 genes) in expression levels postinfection with few exceptions (five genes) e.g. TNF receptor associated factor 1 (Traf1) (Figure 4 and Table S2). Differentially expressed genes were associated with both innate and early adaptive immune response. Among those associated with early immune response, Il1a, Il1rn, prostaglandin E synthase (Ptges), and Ptges2 were up regulated in susceptible strains, whereas their levels decreased in resistant strains. Several of the differentially expressed genes show polymorphisms as SNPs between the parental strains B6 and D2, suggesting that these polymorphic genes modulate differential response to infection (Table S3). The differential expression of IL-1a was confirmed at the protein level (data not shown).
We parsed the differentially expressed genes into pathways, using IPA, IL-1 and prostaglandins were key early response molecules modulating susceptibility to severe GAS sepsis in two the mapped networks, which are shown merged in Figure 5. The first network (P,10 227 ) comprised of genes related to lipid metabolism and innate immunity, e.g. Il1a, Il1rn, Ptges, while second network (P,0.01) contained genes modulating nucleic acid metabolism, energy production and host responses to injury e.g. Ectonucleoside triphosphate diphosphohydrolase 2 (Entpd2) ( Figure 5).

Discussion
It has been established that networks of multiple pathways, rather than a single gene, modulate traits and affect susceptibility and outcomes of virtually all diseases. The ARI strains used in this study afford one of the best unbiased forward genetics approaches to determine how different combinations of polymorphic genes interact to shape disease phenotypes. Using this panel of genetically diverse reference population, we were able to map QTLs modulating specific phenotypes associated with severe GAS sepsis.
GAS causes a wide range of diseases depending on multiple factors including site and route of infection, interplay of the pathogen virulence factors with host immune defenses that are affected by the host immune status and difference in the genetic make up of the host [6]. Thus, these bacteria represent a good model to explore the impact of host complex traits on susceptibility to infections. Previous studies have shown that host-pathogen interactions modulate the severity of GAS sepsis [5,6,9,10], and we found that patients with GAS sepsis expressing HLA class II DRB1*15/DQB1*06 (DR15/DQ6) haplotype are protected from severe GAS sepsis, whereas those with HLA class II DRB1*14/ DQB1*05 (DR14/DQ5) haplotype are at high risk for developing severe and often fatal forms of the disease [6]. The strong association between disease severity and HLA class II allelic polymorphism is primarily related to the differential ability of HLA class II alleles to present SAgs to T cells, where presentation of Strep SAgs by the protective HLA class II alleles results in a significantly attenuated response as compared to their presentation by the neutral or high-risk alleles [6,8,21]. While this association made perfect biological sense based on the known central role of SAgs in the human disease, mice are not susceptible to SAgs due to an inherent lower affinity of mouse MHC class II molecules to GAS SAgs. The role of GAS SAgs can be well investigated in HLA class II transgenic mice as others and we have reported [8,12,22,23]. However, due to the overwhelming response to SAgs in these mice, it is difficult to use them to dissect host response to other GAS virulence factors in the disease process. For this reason, our present ARI mouse model of sepsis is ideal for identifying host genetic variations, besides the MHC class II antigens, that may also contribute to differences in GAS sepsis severity.
We invested time and effort to standardize the GAS infection model using a large number of BXD mice (n = 717) and this allowed us to optimize infection dose and identify significant covariates (e.g. age and sex) needed to be accounted for in our final analysis [20]. Although GAS strains can vary considerably with regards to virulence, we showed that the same virulent strain could cause diseases with starkly different severity in humans [5,6]. The strain used in this study is a hypervirulent derivative of the M1T1 clonal strain that emerged in the 1980's at the same time that the severe forms of the invasive GAS infections resurged [3,[24][25][26]. Initial studies [20] identified an optimal infection dose of 261610 7 CFU/mouse, and indicated the need to use mice with an age range of 40-120 days for linear correlation with survival. In addition, these pilot studies determined that sex has insignificant effect in this GAS sepsis model [20] and revealed a stark variation among various BXD strains used with respect to their susceptibility to severe sepsis. However, precise mapping of QTLs required that we study more BXD strains and include more mice per BXD strain to obtain statistical power.
With a total of 30 BXD strains and an average of 15 mice per strain, we mapped QTLs on Chr 2 that modulate severity to GAS sepsis, measured by comparing mice survival post-infection, bacterial load in blood, and bacterial dissemination to spleen across the BXD strains. Inasmuch as the BXD strains are heavily genotyped with more than 3600 genomic markers, identifying QTLs is relatively straightforward. The mapped QTLs on Chr 2 harbor a relatively large number of candidate genes associated with various functional networks and signaling pathways including nuclear factor-kB (NF-kB) and p38 mitogen activated protein kinases (MAPK) pathways, proliferation of immune cells, and eicosanoid signaling. Such output is typical of unbiased genomewide analysis studies, and required further analyses to determine which of these genes are the key modulators of the studied trait.
To narrow down the gene list to a handful of genes that can be experimentally validated, we used multiple methods including linkage, gene ontology, and differential gene expression analyses. Our quantitative PCR analysis of 37 representative candidate  genes showed that 28 genes were differentially expressed in selected susceptible and resistant strains post-infection. These differentially expressed genes fell into three main categories, genes associated with innate and adaptive immune response, and genes associated with apoptosis. Differentially expressed genes associated with innate immune response were both pro-and anti-inflammatory as well as adaptive immune response genes associated with T and B cell proliferation and differentiation, cell signaling and antigen processing and presentation.
Differentially expressed genes associated with innate immune response were either related to pro-or anti-inflammatory responses. Both pro-and anti-inflammatory associated genes were up-regulated in the selected susceptible strains post-infection, and this is in agreement with a recent study by Goldmann, et al. [27] who showed a mixed pro-and anti-inflammatory response belonging to M1 and M2 macrophage phenotypes in response to GAS infection. This increase in both pro-and antiinflammatory response could be attributed to homeostatic mechanisms where, for example, the increase in Il1 levels in the susceptible strains was accompanied by an increase in its endogenous antagonist Il1rn. This was not the case in the resistant strains that showed decreased levels of expression of pro-and antiinflammatory related genes both pre-and post-infection. These findings suggest that susceptibly to GAS sepsis is associated with an overzealous innate immune response as observed in susceptible BXD strains only. These results mirror previous findings in humans, where association of exaggerated inflammatory responses, including IL-1, with susceptibility to GAS sepsis was detected [6,28,29]. However, unlike what we found in this mouse model, human responses are dominated by high levels of IFN-c and TNFß, presumably because the human disease is driven primarily by SAgs [29,30].
In general, differentially expressed genes associated with early adaptive immune responses showed a pattern of decrease in expression levels in both susceptible and resistant strains with the exception of anaphase promoting complex subunit 2 (Anapc2) that was slightly increased in susceptible strains post-infection ( Figure 5). By contrast, several genes associated with pro-and anti-apoptotic response were differentially expressed in the selected susceptible and resistant strains post-infection ( Figure 5). Apoptosis in streptococcal pathogenesis is affected by interacting factors including, context of infection [31], cells undergoing apoptosis [32][33][34], for example, apoptosis aids in the clearing of infection if macrophages are undergoing apoptosis, while it would be harmful to the host if lymphocytes are undergoing apoptosis. Other factors include, whether the bacteria is internalized or extracellular [35] and accordingly the type of apoptosis pathways activated [36]. In our murine GAS sepsis model, we have measured expression levels in whole spleens, which involved the response of various cells including macrophages, T and B lymphocytes, dendritic and sepsis form susceptible to more resistant. Resistant strains show a pattern of accumulation of alleles inherited from resistant parent (B) C57Bl/6J (blue bars) while the susceptible strains show a pattern of alleles from susceptible parent (D) DBA/2J (red bars), with the exception of BXD94 strain, heterozygous (beige bars) at the QTL region. (C) Haplotype maps of the second QTL region on Chr 2 between 125-150 Mb across BXD strains used in the population-based survival experiments showing the pattern of alleles inherited from each parent at region of interest on Chr 2. The different BXD strains are rank-ordered according to their susceptibility to GAS sepsis form susceptible to more resistant. BXD genotype data set can be downloaded from www.genenetwork.org/genotypes/BXD.geno. doi:10.1371/journal.ppat.1000042.g003 endothelial cells etc. Consequently, we expected to observe a mixed response; however, in our ongoing studies, we are examining possible alterations in splenic population profiles postinfection in the various BXD strains to determine which cell types are responsible for the major differences in cytokine levels seen post-infection and to dissect the role of different cell populations in this GAS sepsis model. Another interesting observation was that the relative expression level of genes measured in the selected resistant strains showed a pattern of decrease post-infection, with the exception of five genes, heat shock 70KDa protein 5 (Hspa5), Traf1, Traf2, Notch gene homolog 1 (Notch1), and signal-regulatory protein alpha (Sirpa). It is worthy to note the link between these genes is as they are involved directly and indirectly with activation of early adaptive response. Hspa5 is an Hsp70, which is associated with cytoprotection, antiapoptosis, and anti-inflammation [37][38][39], and has been associated with immune response to sepsis [40,41]. Notch1 has been associated with the signaling involved in regulation of lymphocytes development and activation to effector cells [42], natural killer cells development [43][44][45] and recently Notch1 was associated with macrophage activation [46].We took into consideration in our experimental design that stress might alter the expression of stress related genes especially heat shock proteins and chemokines, therefore, we subjected control mice to the same stress as infected mice by injecting control mice with saline, so that any change in expression levels would be accounted to GAS sepsis.
Among the differentially expressed genes, four genes, Il1, Il1rn, Ptges, and Ptges2, showed marked up regulation in susceptible strains, while showed no change or slight decrease in levels in resistant strains post-infection ( Figure 5). Expression of Ptges and Ptges2 genes increase is induced by the increased levels of Il1 [47], which is an indirect effect of the activation of IFN-c ( Figure 5).
Ptges and Ptges2 are prostaglandins synthases for the lipid inflammatory mediators PGE2, which along with platelet activating factor and leukotriens, mediate vasodilatation in the early response to inflammation [48,49]. Vasodilatation in turn leads to hypotension, a hallmark of STSS. Although the role of prostaglandins in inflammation and immune response has long been studied, their role in the immune response to infectious diseases has been lately pursued [50][51][52][53][54][55]. Increased production of prostaglandins has been associated with various Gram positive bacterial infections including Streptococcus suis [53], group B streptococcal [55], and GAS skin infections [54].
In conclusion, our holistic approach of studying the genetic basis of differential susceptibility to GAS sepsis revealed loci on Chr 2 that harbor major immune modulators. In the present study, we examined the interactions of pathogen multiple virulence factors with the host immune system in an in vivo model of sepsis using a genetically diverse reference population. This shed light on a network of host genes modulating variation in GAS severity, which includes cytokines, pro-and anti-inflammatory mediators, and genes associated with apoptosis and early adaptive immune response. Our ongoing detailed studies to identify interactive molecular pathways contributing to the complex trait of GAS sepsis will undoubtedly help us dissect the various mechanisms by which the host interacts with the bacteria in vivo, resulting in resistance or increased susceptibility to severe GAS sepsis.

Mice
We generated the BXD advanced recombinant inbred (ARI) mice at UTHSC by crossing B6 and D2 mice to generate F1 hybrids, which were crossed to generate F2 progeny, each with random patterns of recombination. Random crossing of F2 mice generated F3 and so on, till F11 generation, after which we designated pairs of F11 hybrids as parents of each BXD line, and inbred them by sib mating for .20 generations to achieve homozygosity for each genetically distinct BXD line. This breeding scheme was done to increase recombination events resulting in roughly double the number of recombinations per strain compared to conventional RI strains [56,57].
The genomes of the B6 and D2 parental strains have been sequenced and a database of their SNPs is available at the Gene Network (GN) web site (www.genenetwork.org/webqtl/snpBrowser. py). Simple sequence length polymorphism (SSLP) markers were typed for all BXD RI strains as previously described [56]. The BXD progeny is genotyped at 13377 SNPs and microsatellite markers [58], a selected subset of 3795 SNPs and microsatellite markers used by GN BXD genotype dataset for mapping traits, can be downloaded at www.genenetwork.org/genotypes/BXD.geno.
We recently standardized the model of BXD ARI mice for use in GAS infection studies [20]. In the current study, a total of 696 mice were used, from which 183 flagged mice were excluded based on predetermined criteria as previously detailed in [20].
All procedures involving mouse tissues were approved by the institutional animal care and use committee at the UTHSC.

Experimental design
A) Population-based experiments. Groups of 5-26 mice from a total of 30 BXD lines and their parental strains (B6 and D2) were injected via tail vein with 261.5610 7 CFU/mouse of a hypervirulent form of the clinical isolate GAS 5448 (M1T1) [5,24,59] as detailed previously [20]. Mortality and weight loss were recorded every 8 h for the ensuing 6 days. To normalize across experiments, we inspected survival days distribution clusters for each experiment and determined multimodal distribution and boundaries of each cluster for a total of three clusters: susceptible, intermediate, and resistant. Survival days within each cluster were then converted into a survival index ranging from 0.25-1, 1.25-2, and 2.25-3 for susceptible, intermediate, and resistant clusters respectively. The survival index was assigned to each mouse irrespective of its strain. Indices for each strain, across experiments were then corrected for significant covariates (age, sex, body weight, and inocula) using multiple regression analyses.
B) Phenotype assessment experiments. We designed experiments for the assessment of survival, bacteremia, and bacterial dissemination to spleen. We assessed relative mice survival and bacteremia in the same experiment. We measured bacteremia as CFU/ml by culturing serial dilutions of blood drawn from mice tails 24 h post-injection. We determined bacterial load in spleen by homogenizing spleens of expired mice using tissue homogenizer, TH (Omni International, Marietta, GA). Each tissue homogenate was serially diluted 10 folds to 10 26 fold, and each dilution was cultured for determination of bacterial load in spleens (CFU/ml) that was then normalized to spleen weight and expressed as CFU/spleen.

QTL Mapping
We performed QTL mapping using web-based complex trait analysis available on the GN website and the mapping module which analyzes phenotypes in context of mouse genotypic differences. Interval mapping evaluates potential QTL at regular intervals and estimates the significance at each location using 1000 permutation tests [60]. We performed three sets of analyses using strain means for the following three variables: (1) corrected relative survival index, described above, (2) log bacterial load in blood 24 h post injection, and (3) log bacterial load in spleen at expiration.

Quantitative PCR analysis for target genes expression
We investigated the differential expression of target genes in spleen of infected vs. control PBS-injected mice at selected time points, we selected strains based on their susceptibility, BXD61 and 90 representing susceptible strains, and BXD73 and 87, representing resistant strains. We performed 2-3 biological replicates of each set of paired susceptible and resistant (n = 6-8 per strain). Mice were sacrificed 40 h post i.v. injection with 261.5610 7 CFU/mouse of clinical isolate GAS 5448 (M1T1) and RNA from individual mice was extracted from spleens. Bacteremia was determined as described above. We isolated RNA using RNA-STAT 60 method and when necessary, we purified RNA samples using RNeasy mini kit clean up columns (Qiagen, Valencia, CA). We pooled RNA samples per strain with A260/280 ratios$1.8 for cDNA synthesis with SuperScript III reverse transcriptase kit (Invitrogen, Carlsbad CA) using oligo dT primers. We designed real time PCR assays, hydrolysis probes, and gene specific primers that span long introns to distinguish cDNA from genomic DNA using primer design online software universal probe library (UPL) at www.roche-applied-science.com/sis/rtpcr/upl/index.jsp. We performed quantitative TaqMan PCR on light cycler LC480 (Roche Applied Science, Indianapolis, IN). We used the mouse housekeeping gene, hypoxanthine guanine phosphoribosyl transferase (Hprt1) as an endogenous control to which we normalized gene expression data. Primer sequences are listed in Table S1. Samples were analyzed in triplicates for each of 2-4 biological replicates. We used delta delta Ct (threshold cycle) method for calculating relative expression levels expressed as fold differences between pre-and post-infection values for each gene analyzed. Student t-test was used to assess statistical significance.

Identification of differentially expressed genes in the mapped interval and bioinformatics functional pathways analyses
We generated functional analyses of genes within the QTLs using Ingenuity pathways analysis (IPA) (www.ingenuity.com). Each data set containing gene identifiers was uploaded into the online application, and each gene was overlaid onto a molecular network developed from information contained in the ingenuity pathways database. Networks of genes were then generated based on their connectivity, and we chose the top 50 significant networks. The significance of the association between the data set and the pathways was measured in two ways: (1) the ratio of the number of genes from the data set that map to the pathway divided by the total number of genes that map to the pathway; and (2) by Fischer's exact test with P,0.001.
Statistical analysis. We used the DataDesk 6.2 (www. datadesk.com) to calculate mean survival indices, corrected survival indices, and correlation coefficients. Covariates association was evaluated for significant associations by Spearman correlation as described previously [20]. Fischer exact test was used to analyze association of the analyzed differentially expressed genes with specific pathways.

Web site URL
Our data sets stored in WebQTL can be found at Gene Network (www.genenetwork.org) under BXD published phenotypes record ID 10836.